activity
20242026
collaborators

13 papers

cs.RO2026

CoDA: Co-Evolution of Data Engines and Action Policies

Tianjiao Yu, Zhilin Ou, Yifan Shen +5

Generative simulation can automate robot environment construction, but physically valid environments are not necessarily useful for training. The challenge is to identify environme…

cs.AI2026

OpenDepth: Repurposing Image Editing Priors for Robust Open-World Monocular Depth Estimation

Mengke Zhang, Tianyi Yang, Xinzhuo Li +1

Recently, the integration of generative priors has revolutionized Monocular Depth Estimation (MDE), achieving state-of-the-art (SOTA) performance with remarkably high data efficien…

cs.CV2026

GaussianSeed: Hierarchical Gaussian Seeding for High-Resolution 3D Occupancy Prediction

Xinzhuo Li, Xianghui Pan, Jiayuan Du +4

Vision-centric 3D occupancy prediction provides dense scene representations essential for autonomous driving and robotic navigation, yet existing methods struggle to scale to high…

cs.CV2026

ELSA3D: Elastic Semantic Anchoring for Unified 3D Understanding and Generation

Tianjiao Yu, Xinzhuo Li, Yifan Shen +4

Unified 3D foundation models aspire to generate 3D assets and reason about them in language within a single backbone, but their text-3D interaction remains largely implicit. Existi…

cs.CV2026

CogniRoute: Learning to Route Social Evidence in Omni-Modal Models

Yifan Shen, Pei Tian, Xinzhuo Li +8

Omni-modal models can ingest video, audio, and text, but unified access to multiple modalities does not guarantee that a model uses the right evidence. This gap is especially prono…

cs.RO2026

VTAM: Efficient Visuo-Tactile World-Action Models for Contact-Rich Manipulation without Tactile Pretraining

Haoran Yuan, Weigang Yi, Zhenyu Zhang +11

Tactile vision-language-action models (VLAs) and tactile world-action models (WAMs) are a promising new paradigm for contact-rich manipulation, but existing models either underutil…

cs.CV2026

EgoForge: Goal-Directed Egocentric World Simulator

Yifan Shen, Jiateng Liu, Xinzhuo Li +9

Generative world models have shown promise for simulating dynamic environments, yet egocentric video remains challenging due to rapid viewpoint changes, frequent hand-object intera…

cs.CV2026

DreamPartGen: Semantically Grounded Part-Level 3D Generation via Collaborative Latent Denoising

Tianjiao Yu, Xinzhuo Li, Muntasir Wahed +4

Understanding and generating 3D objects as compositions of meaningful parts is fundamental to human perception and reasoning. However, most text-to-3D methods overlook the semantic…

cs.CL2026

SafeCRS: Personalized Safety Alignment for LLM-Based Conversational Recommender Systems

Haochang Hao, Yifan Xu, Xinzhuo Li +2

Current LLM-based conversational recommender systems (CRS) primarily optimize recommendation accuracy and user satisfaction. We identify an underexplored vulnerability in which rec…

cs.CV2026

Toward Cognitive Supersensing in Multimodal Large Language Model

Boyi Li, Yifan Shen, Yuanzhe Liu +12

Multimodal Large Language Models (MLLMs) have achieved remarkable success in open-vocabulary perceptual tasks, yet their ability to solve complex cognitive problems remains limited…

cs.CV2025

CoRe3D: Collaborative Reasoning as a Foundation for 3D Intelligence

Tianjiao Yu, Xinzhuo Li, Yifan Shen +2

Recent advances in large multimodal models suggest that explicit reasoning mechanisms play a critical role in improving model reliability, interpretability, and cross-modal alignme…

cs.CV2025

Counterfactual Segmentation Reasoning: Diagnosing and Mitigating Pixel-Grounding Hallucination

Xinzhuo Li, Adheesh Juvekar, Jiaxun Zhang +6

Segmentation Vision-Language Models (VLMs) have significantly advanced grounded visual understanding, yet they remain prone to pixel-grounding hallucinations, producing masks for i…

cs.CV2024

PRIMA: Multi-Image Vision-Language Models for Reasoning Segmentation

Muntasir Wahed, Kiet A. Nguyen, Adheesh Sunil Juvekar +6

Despite significant advancements in Large Vision-Language Models (LVLMs)' capabilities, existing pixel-grounding models operate in single-image settings, limiting their ability to…